The International Journal of Applied Spatial Econometrics (IJASE) is an international, peer-reviewed journal dedicated to advancing applied research in spatial and spatiotemporal econometrics. It provides a platform for high-quality empirical, methodological, and policy-relevant studies using georeferenced data to address complex regional, urban, environmental, and socio-economic challenges, with the aim of improving inference, predictive performance, and evidence-based decision-making.

A distinctive feature of The International Journal of Applied Spatial Econometrics (IJASE) is its emphasis on the integration of spatial econometrics with machine learning and advanced data analytics, enabling the analysis of large-scale, high-dimensional datasets and the identification of complex, nonlinear relationships while maintaining interpretability and methodological rigor. The journal also prioritizes spatiotemporal modelling, recognizing its importance in capturing dynamic processes, diffusion mechanisms, and evolving spatial patterns.

The International Journal of Applied Spatial Econometrics (IJASE) serves a broad interdisciplinary audience, including economists, regional scientists, geographers, urban planners, and data scientists. It is committed to promoting reproducible research, open data practices, and transparent analytical workflows.

In line with its applied and interdisciplinary focus, the International Journal of Applied Spatial Econometrics (IJASE) also highlights the rapid advancement of analytical tools supporting spatial and spatiotemporal econometric research, including:

-        GIS & Visualization Tools

-        Spatial Econometric & Statistical Tools

-        Spatiotemporal Modelling Tools

-        Machine Learning & Artificial Intelligence for Spatial Data tools

The editorial board of the International Journal of Applied Spatial Econometrics (IJASE), therefore, particularly welcomes:

• empirical and methodological advances in spatial and spatiotemporal econometrics;
• studies on spatial interdependencies in economic activity, including regional disparities and cross-border dynamics;
• computational and data-driven approaches for analyzing georeferenced economic data;
• applications combining spatial and spatiotemporal econometric methods with machine learning techniques for modelling complex processes;
• interdisciplinary research connecting spatial econometrics with fields such as environmental studies, transportation, urban planning, health economics, and the digital economy;
• applied research using novel data sources, including geospatial big data, remote sensing, and high-frequency indicators;
• policy-oriented studies that apply spatial methods to regional development, public health, and economic decision-making.

The journal also welcomes contributions that replicate or document robustness tests of studies within the scope of the journal.